Temporal clustering of precipitation for detection of potential landslides
Abstract Landslides are complex phenomena that cause important impacts in vulnerable areas, including the destruction of infrastructure, environmental damage, and loss of life. The occurrence of landslide events is often triggered by rainfall episodes, single and intense ones or multiple ones occurring in sequence, i.e., clustered in time. Landslide prediction is typically obtained via process-based or empirical thresholds. Here, we develop a new approach that uses information on the temporal clustering of rainfall to detect landslide events and compare it with the use of classical empirical rainfall thresholds. In addition, we evaluate the performance of the two approaches combined together as a case study in the region of Lisbon in Portugal. We consider a dataset that categorizes landslides into shallow and deep events and a review of empirical rainfall thresholds that makes a good benchmark for testing our novel method. We show that the new approach based on temporal clustering overall has a good power of detecting landslide events but has a skill comparable with the classic rainfall threshold method. While there is no clear outperformance of one method, the novel clustering-based method has a higher sensitivity despite a lower precision than the threshold-based method. For all approaches, the potential detection is better for deep landslides than for shallow ones. The results of this study could help to improve the prediction of rainfall-triggered landslides.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Temporal clustering of precipitation for detection of potential landslides ; volume:24 ; number:8 ; year:2024 ; pages:2689-2704 ; extent:16
Natural hazards and earth system sciences ; 24, Heft 8 (2024), 2689-2704 (gesamt 16)
- Creator
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Banfi, Fabiola
Bevacqua, Emanuele
Rivoire, Pauline
Oliveira, Sérgio C.
Pinto, Joaquim
Ramos, Alexandre M.
De Michele, Carlo
- DOI
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10.5194/nhess-24-2689-2024
- URN
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urn:nbn:de:101:1-2408150434569.976877495086
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:56 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Banfi, Fabiola
- Bevacqua, Emanuele
- Rivoire, Pauline
- Oliveira, Sérgio C.
- Pinto, Joaquim
- Ramos, Alexandre M.
- De Michele, Carlo
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A climatology of sub-seasonal temporal clustering of extreme precipitation in Switzerland and its impacts
Spatial–temporal clustering of tornadoes
Conditional clustering of temporal expression profiles
Spatio-temporal clustering
Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides
Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory
Seismic and geologic controls on spatial clustering of landslides in three large earthquakes
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Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility
Inventory of shallow landslides triggered by extreme precipitation in July 2023 in Beijing, China
The ITAlian rainfall-induced LandslIdes CAtalogue, an extensive and accurate spatio-temporal catalogue of rainfall-induced landslides in Italy
Temporal clustering of precipitation for detection of potential landslides
A climatology of sub-seasonal temporal clustering of extreme precipitation in Switzerland and its impacts
Spatial–temporal clustering of tornadoes
Conditional clustering of temporal expression profiles
Spatio-temporal clustering
Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides
Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory
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Seismic and geologic controls on spatial clustering of landslides in three large earthquakes
Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility
Inventory of shallow landslides triggered by extreme precipitation in July 2023 in Beijing, China